The impact of the clean development mechanism (CDM) on the cost price of wind power electricity : a China study

As a renewable energy source, wind power is playing an increasingly important role in China’s electricity supply. Meanwhile, China is also the world’s largest market for Clean Development Mechanism (CDM) wind power projects. Based on the data of 27 wind power projects of Inner Mongolia registered with the Executive Board of the United Nations (EB) in 2010, this paper constructs a financial model of Net Present Value (NPV) to analyze the cost of wind power electricity. A sensitivity analysis is then conducted to examine the impact of different variables with and without Certified Emission Reduction (CER) income brought about by the CDM. It is concluded that the CDM, along with static investment and annual wind electricity production, is one of the most significant factors in promoting the development of wind power in China. Additionally, wind power is envisaged as a practical proposition for competing with thermal power if the appropriate actions identified in the paper are made.

Turbulent flow structure in experimental laboratory wind-generated gravity waves

This paper is the third part of a report on systematic measurements and analyses of wind-generated water waves in a laboratory environment. The results of the measurements of the turbulent flow on the water side are presented here, the details of which include the turbulence structure, the correlation functions, and the length and velocity scales. It shows that the mean turbulent velocity profiles are logarithmic, and the flows are hydraulically rough. The friction velocity in the water boundary layer is an order of magnitude smaller than that in the wind boundary layer. The level of turbulence is enhanced immediately beneath the water surface due to micro-breaking, which reflects that the Reynolds shear stress is of the order u *w 2. The vertical velocities of the turbulence are related to the relevant velocity scale at the still-water level. The autocorrelation function in the vertical direction shows features of typical anisotropic turbulence comprising a large range of wavelengths. The ratio between the microscale and macroscale can be expressed as λ/Λ=a Re Λ n, with the exponent n slightly different from -1/2, which is the value when turbulence production and dissipation are in balance. On the basis of the wavelength and turbulent velocity, the free-surface flows in the present experiments fall into the wavy free-surface flow regime. The integral turbulent scale on the water side alone underestimates the degree of disturbance at the free surface. © 2012 Elsevier B.V.

Wind-generated waves for laboratory studies /

"August 1976."

Canal bank erosion due to wind-generated water waves : progress report I /

Mode of access: Internet.

Climate and energy scenarios for Ireland to 2050 using the Irish TIMES energy systems model

Due to growing concerns regarding the anthropogenic interference with the climate system, countries across the world are being challenged to develop effective strategies to mitigate climate change by reducing or preventing greenhouse gas (GHG) emissions. The European Union (EU) is committed to contribute to this challenge by setting a number of climate and energy targets for the years 2020, 2030 and 2050 and then agreeing effort sharing amongst Member States. This thesis focus on one Member State, Ireland, which faces specific challenges and is not on track to meet the targets agreed to date. Before this work commenced, there were no projections of energy demand or supply for Ireland beyond 2020. This thesis uses techno-economic energy modelling instruments to address this knowledge gap. It builds and compares robust, comprehensive policy scenarios, providing a means of assessing the implications of different future energy and emissions pathways for the Irish economy, Ireland’s energy mix and the environment. A central focus of this thesis is to explore the dynamics of the energy system moving towards a low carbon economy. This thesis develops an energy systems model (the Irish TIMES model) to assess the implications of a range of energy and climate policy targets and target years. The thesis also compares the results generated from the least cost scenarios with official projections and target pathways and provides useful metrics and indications to identify key drivers and to support both policy makers and stakeholder in identifying cost optimal strategies. The thesis also extends the functionality of energy system modelling by developing and applying new methodologies to provide additional insights with a focus on particular issues that emerge from the scenario analysis carried out. Firstly, the thesis develops a methodology for soft-linking an energy systems model (Irish TIMES) with a power systems model (PLEXOS) to improve the interpretation of the electricity sector results in the energy system model. The soft-linking enables higher temporal resolution and improved characterisation of power plants and power system operation Secondly, the thesis develops a methodology for the integration of agriculture and energy systems modelling to enable coherent economy wide climate mitigation scenario analysis. This provides a very useful starting point for considering the trade-offs between the energy system and agriculture in the context of a low carbon economy and for enabling analysis of land-use competition. Three specific time scale perspectives are examined in this thesis (2020, 2030, 2050), aligning with key policy target time horizons. The results indicate that Ireland’s short term mandatory emissions reduction target will not be achieved without a significant reassessment of renewable energy policy and that the current dominant policy focus on wind-generated electricity is misplaced. In the medium to long term, the results suggest that energy efficiency is the first cost effective measure to deliver emissions reduction; biomass and biofuels are likely to be the most significant fuel source for Ireland in the context of a low carbon future prompting the need for a detailed assessment of possible implications for sustainability and competition with the agri-food sectors; significant changes are required in infrastructure to deliver deep emissions reductions (to enable the electrification of heat and transport, to accommodate carbon capture and storage facilities (CCS) and for biofuels); competition between energy and agriculture for land-use will become a key issue. The purpose of this thesis is to increase the evidence-based underpinning energy and climate policy decisions in Ireland. The methodology is replicable in other Member States.

REDUCING DIESEL DEPENDENCE IN NUNAVUT: INTEGRATING RENEWABLE ENERGY TECHNOLOGIES THROUGH POLICY ACTIONS

In the last fifty years, Nunavut has developed a deep dependence on diesel for virtually all of its energy needs, including electricity. This dependence has created a number of economic, environmental and health related challenges in the territory, with an estimated 20% of the territory’s annual budget being spent on energy, thereby limiting the Government of Nunavut’s ability to address other essential infrastructure and societal needs, such as education, nutrition and health care and housing. One solution to address this diesel dependency is the use of renewable energy technologies (RETs), such as wind, solar and hydropower. As such, this thesis explores energy alternatives in Nunavut, and through RETScreen renewable energy simulations, found that solar power and wind power are technically viable options for Nunavut communities and a potentially successful means to offset diesel-generated electricity in Nunavut. However, through this analysis it was also discovered that accurate data or renewable resources are often unavailable for most Nunavut communities. Moreover, through qualitative open-ended interviews, the perspectives of Nunavut residents with regards to developing RETs in Nunavut were explored, and it was found that respondents generally supported the use of renewable energy in their communities, while acknowledging that there still remains a knowledge gap among residents regarding renewable energy, stemming from a lack of communication between the communities, government and the utility company. In addition, the perceived challenges, opportunities and gaps that exist with regards to renewable energy policy and program development were discussed with government policy-makers through further interviews, and it was discovered that often government departments work largely independently of each other rather than collaboratively, creating gaps and oversights in renewable energy policy in Nunavut. Combined, the results of this thesis were used to develop a number of recommended policy actions that could be undertaken by the territorial and federal government to support a shift towards renewable energy in order to develop a sustainable and self-sufficient energy plan in Nunavut. They include: gathering accurate renewable resource data in Nunavut; increasing community consultations on the subject of renewable energy; building strong partnerships with universities, colleges and industry; developing a knowledge sharing network; and finally increasing accessibility to renewable energy programs and policies in Nunavut.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

Entwicklung eines Windleistungprognosemodells zur Verbesserung der Kraftwerkseinsatzplanung

Die Maßnahmen zur Förderung der Windenergie in Deutschland haben wichtige Anstöße zur technologischen Weiterentwicklung geliefert und die Grundlagen für den enormen Anlagenzubau geschaffen. Die installierte Windleistung hat heute eine beachtliche Größenordnung erreicht und ein weiteres Wachstum in ähnlichen Dimensionen ist auch für die nächsten Jahre zu erwarten. Die aus Wind erzeugte elektrische Leistung deckt bereits heute in einigen Netzbereichen die Netzlast zu Schwachlastzeiten. Dies zeigt, dass die Windenergie ein nicht mehr zu vernachlässigender Faktor in der elektrischen Energieversorgung geworden ist. Im Rahmen der Kraftwerkseinsatzplanung sind Betrag und Verlauf der Windleistung des folgenden Tages mittlerweile zu wichtigen und zugleich schwierig zu bestimmenden Variablen geworden. Starke Schwankungen und falsche Prognosen der Windstromeinspeisung verursachen zusätzlichen Bedarf an Regel- und Ausgleichsleistung durch die Systemführung. Das im Rahmen dieser Arbeit entwickelte Prognosemodell liefert die zu erwartenden Windleistungen an 16 repräsentativen Windparks bzw. Gruppen von Windparks für bis zu 48 Stunden im Voraus. Aufgrund von prognostizierten Wetterdaten des deutschen Wetterdienstes (DWD) werden die Leistungen der einzelnen Windparks mit Hilfe von künstlichen neuronalen Netzen (KNN) berechnet. Diese Methode hat gegenüber physikalischen Verfahren den Vorteil, dass der komplexe Zusammenhang zwischen Wettergeschehen und Windparkleistung nicht aufwendig analysiert und detailliert mathematisch beschrieben werden muss, sondern anhand von Daten aus der Vergangenheit von den KNN gelernt wird. Das Prognosemodell besteht aus zwei Modulen. Mit dem ersten wird, basierend auf den meteorologischen Vorhersagen des DWD, eine Prognose für den Folgetag erstellt. Das zweite Modul bezieht die online gemessenen Leistungsdaten der repräsentativen Windparks mit ein, um die ursprüngliche Folgetagsprognose zu verbessern und eine sehr genaue Kurzzeitprognose für die nächsten drei bis sechs Stunden zu berechnen. Mit den Ergebnissen der Prognosemodule für die repräsentativen Standorte wird dann über ein Transformationsmodell, dem so genannten Online-Modell, die Gesamteinspeisung in einem größeren Gebiet berechnet. Das Prognoseverfahren hat seine besonderen Vorzüge in der Genauigkeit, den geringen Rechenzeiten und den niedrigen Betriebskosten, da durch die Verwendung des bereits implementierten Online-Modells nur eine geringe Anzahl von Vorhersage- und Messstandorten benötigt wird. Das hier vorgestellte Prognosemodell wurde ursprünglich für die E.ON-Netz GmbH entwickelt und optimiert und ist dort seit Juli 2001 im Einsatz. Es lässt sich jedoch auch leicht an andere Gebiete anpassen. Benötigt werden dazu nur die Messdaten der Leistung ausgewählter repräsentativer Windparks sowie die dazu gehörenden Wettervorhersagen, um die KNN entsprechend zu trainieren.

Methodology for estimating building integrated photovoltaics electricity production under shadowing conditions and case study

Building integrated photovoltaic (BIPV) systems are a relevant application of photovoltaics. In countries belonging to the International Energy Agency countries, 24% of total installed PV power corresponds to BIPV systems. Electricity losses caused by shadows over the PV generator have a significant impact on the performance of BIPV systems, being the major source of electricity losses. This paper presents a methodology to estimate electricity produced by BIPV systems which incorporates a model for shading losses. The proposed methodology has been validated on a one year study with real data from two similar PV systems placed on the south façade of a building belonging to the Technical University of Madrid. This study has covered all weather conditions: clear, partially overcast and fully overcast sky. Results of this study are shown at different time scales, resulting that the errors committed by the best performing model are below 1% and 3% in annual and daily electricity estimation. The use of models which account for the reduced performance at low irradiance levels also improves the estimation of generated electricity.

Gust wind tunnel study on ballast pick-up by high-speed trains

This paper describes the experimental setup, procedure, and results obtained, concerning the dynamics of a body lying on a floor, attached to a hinge, and exposed to an unsteady flow, which is a model of the initiation of rotational motion of ballast stones due to the wind generated by the passing of a high-speed train. The idea is to obtain experimental data to support the theoretical model developed in Sanz-Andres and Navarro-Medina (J Wind Eng Ind Aerodyn 98, 772–783, (2010), aimed at analyzing the initial phase of the ballast train-induced-wind erosion (BATIWE) phenomenon. The experimental setup is based on an open circuit, closed test section, low-speed wind tunnel, with a new sinusoidal gust generator mechanism concept, designed and built at the IDR/UPM. The tunnel’s main characteristic is the ability to generate a flow with a uniform velocity profile and sinusoidal time fluctuation of the speed. Experimental results and theoretical model predictions are in good agreement.

Comparison of Wildlife Impacts Associated with Uranium In-Situ Recovery and Wind Farms in Wyoming

Wyoming has multiple resources including non-renewable sources, renewable sources, as well as its wildlife. Two of these resources are uranium and wind. Currently wind farms in Wyoming are generating approximately 5 million MW of power, with less of an impact on wildlife than in-situ facilities. In-situ facilities in 2007 produced an estimated 32 million MW of power from uranium, with a greater impact to wildlife than wind farms. Both resources have a great potential in Wyoming and both will have an impact on wildlife. Currently wind farms show less of an impact on wildlife but they are also producing fewer megawatts. The potential for wind-generated energy over the next century shows wildlife impacts will be greater than impacts from ISR facilities.

Effects of Information and Time of Use Pricing on Irish Electricity Demand and Supply

In this dissertation I quantify residential behavior response to interventions designed to reduce electricity demand at different periods of the day. In the first chapter, I examine the effect of information provision coupled with bimonthly billing, monthly billing, and in-home displays, as well as a time-of-use (TOU) pricing scheme to measure consumption over each month of the Irish Consumer Behavior Trial. I find that time-of-use pricing with real time usage information reduces electricity usage up to 8.7 percent during peak times at the start of the trial but the effect decays over the first three months and after three months the in-home display group is indistinguishable from the monthly treatment group. Monthly and bi-monthly billing treatments are not found to be statistically different from another. These findings suggest that increasing billing reports to the monthly level may be more cost effective for electricity generators who wish to decrease expenses and consumption, rather than providing in-home displays. In the following chapter, I examine the response of residential households after exposure to time of use tariffs at different hours of the day. I find that these treatments reduce electricity consumption during peak hours by almost four percent, significantly lowering demand. Within the model, I find evidence of overall conservation in electricity used. In addition, weekday peak reductions appear to carry over to the weekend when peak pricing is not present, suggesting changes in consumer habit. The final chapter of my dissertation imposes a system wide time of use plan to analyze the potential reduction in carbon emissions from load shifting based on the Ireland and Northern Single Electricity Market. I find that CO2 emissions savings are highest during the winter months when load demand is highest and dirtier power plants are scheduled to meet peak demand. TOU pricing allows for shifting in usage from peak usage to off peak usage and this shift in load can be met with cleaner and cheaper generated electricity from imports, high efficiency gas units, and hydro units.